Python Course for Machine learning in Sonarpur Kolkata

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Python is a versatile language that you can use for building all sorts of applications, including machine learning models. in Sonarpur Kolkata, there are many institutes that offer training in Python for machine learning. We provide professional Python course for Machine learning in Sonarpur Kolkata with projects.
These institutes provide comprehensive training that covers all the important aspects of machine learning using Python. The trainers at these institutes are experienced professionals who have in-depth knowledge of both Python and machine learning.
Thus, if you are looking to get started with machine learning in Sonarpur Kolkata, enrolling in a course at one of these institutes would be a good idea.


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    Python for Machine Learing Course Syllabus

    • Welcome to Machine Learning. Here are a few resources to get you started!
    • Welcome to the Machine Learning Section!
    • Supervised Learning Overview
    • Evaluating Performance - Classification Error Metrics
    • Evaluating Performance - Regression Error Metrics
    • Machine Learning with Python

    • Linear Regression Theory
    • model_selection Updates for SciKit Learn 0.18
    • Linear Regression with Python - Part 1
    • Linear Regression with Python - Part 2
    • Linear Regression Project Overview
    • Linear Regression Project Solution

    • Bias Variance Trade-Off

    • Logistic Regression Theory
    • Logistic Regression with Python - Part 1
    • Logistic Regression with Python - Part 2
    • Logistic Regression with Python - Part 3
    • Logistic Regression Project Overview
    • Logistic Regression Project Solutions

    • KNN Theory
    • KNN with Python
    • KNN Project Overview
    • KNN Project Solutions

    • Introduction to Tree Methods
    • Decision Trees and Random Forest with Python
    • Decision Trees and Random Forest Project Overview
    • Decision Trees and Random Forest Solutions Part 1
    • Decision Trees and Random Forest Solutions Part 2

    • SVM Theory
    • Support Vector Machines with Python
    • SVM Project Overview
    • SVM Project Solutions

    • K Means Algorithm Theory
    • K Means Project Overview
    • K Means Project Overview
    • K Means Project Solutions

    • Principal Component Analysis
    • PCA with Python

    • Recommender Systems
    • Recommender Systems with Python - Part 1
    • Recommender Systems with Python - Part 2

    • Natural Language Processing Theory
    • NLP with Python - Part 1
    • NLP with Python - Part 2
    • NLP with Python - Part 3
    • NLP Project Overview
    • NLP Project Solutions

    • Welcome to the Deep Learning Section!
    • Introduction to Artificial Neural Networks (ANN)
    • Perceptron Model
    • Neural Networks
    • Activation Functions
    • Multi-Class Classification Considerations
    • Cost Functions and Gradient Descent
    • Backpropagation
    • TensorFlow vs Keras
    • TF Syntax Basics - Part One - Preparing the Data
    • TF Syntax Basics - Part Two - Creating and Training the Model
    • TF Syntax Basics - Part Three - Model Evaluation
    • TF Regression Code Along - Exploratory Data Analysis
    • TF Regression Code Along - Exploratory Data Analysis - Continued
    • TF Regression Code Along - Data Preprocessing and Creating a Model
    • TF Regression Code Along - Model Evaluation and Predictions
    • TF Classification Code Along - EDA and Preprocessing
    • TF Classification - Dealing with Overfitting and Evaluation
    • TensorFlow 2.0 Project Options Overview
    • TensorFlow 2.0 Project Notebook Overview
    • Keras Project Solutions - Dealing with Missing Data
    • Keras Project Solutions - Dealing with Missing Data - Part Two
    • Keras Project Solutions - Categorical Data
    • Keras Project Solutions - Data PreProcessing
    • Keras Project Solutions - Data PreProcessing
    • Keras Project Solutions - Creating and Training a Model
    • Keras Project Solutions - Model Evaluation
    • Tensorboard

    • Welcome to the Big Data Section!
    • Big Data Overview
    • Spark Overview
    • AWS Account Set-Up
    • EC2 Instance Set-Up
    • SSH with Mac or Linux
    • PySpark Setup
    • Lambda Expressions Review
    • Introduction to Spark and Python
    • RDD Transformations and Actions

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    Why learn Python course for Machine Learning?

    The main reasons why Python is used for machine learning are:

    Python is Flexible: In terms of flexibility, Python is ideal for developers who wish to script applications and websites. This is because Python is a simple language that does not require as much coding skill as other languages.

    Easy to Learn: The main goal of Python is to keep things simple and readable, making it an excellent tool for beginners. Furthermore, Python has a relatively low learning curve, giving programmers more freedom to experiment. As compared to the old languages, Python has fewer lines of code, so programmers can play around more.

    Python is Open Source: Essentially, Python is free and designed to run on Windows and Linux environments. It can also be ported to other platforms as well.

    Large community: Python is widely used in academia and industry. This means there are many analytics libraries available. Additionally, Python users have access to Stack Overflow, mailing lists, and user-contributed code and documentation. Python is becoming more and more popular every day.

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    Hands-on training on the capstone projects

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    Expert mentoring Weekly Tests & Assignments

    Mock-up Exams, and Interviews

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    Exclusive programs for Non-IT professionals

    Why learn Python Course for Machine Learning?

    Python has become one of the most popular programming languages for machine learning because it is easy to learn and use, and has a wide range of libraries and frameworks that are specifically designed for data analysis, scientific computing, and machine learning. Here are some specific reasons why learning Python is beneficial for machine learning:

    Easy to learn and use: Python is a high-level language with a simple syntax that is easy to learn and use, making it a good choice for beginners. It also has a large and supportive community of developers who are willing to help and share their knowledge.

    Wide range of libraries and frameworks: Python has a vast range of libraries and frameworks for data analysis, scientific computing, and machine learning, including NumPy, Pandas, Scikit-learn, TensorFlow, Keras, PyTorch, and more. These libraries and frameworks make it easy to implement machine learning algorithms and work with large datasets.

    Flexibility and scalability: Python is a versatile language that can be used for a variety of applications, including web development, data analysis, and machine learning. It is also scalable, meaning it can handle large datasets and complex computations.

    Demand in the job market: Python is currently one of the most popular programming languages used in machine learning and data science. Learning Python for machine learning can open up many job opportunities in these fields.

    In conclusion, learning Python for machine learning is a smart choice for anyone interested in working with large datasets, implementing machine learning algorithms, and pursuing a career in data science or machine learning.

    Python Is most secure and high-paying programing language in the world